Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations18575
Missing cells19
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 MiB
Average record size in memory679.8 B

Variable types

Text2
DateTime2
Numeric7
Categorical8

Alerts

cubeCapacity is highly overall correlated with cylinder and 3 other fieldsHigh correlation
cylinder is highly overall correlated with cubeCapacity and 3 other fieldsHigh correlation
doorNumber is highly overall correlated with typeHigh correlation
fuel is highly overall correlated with cubeCapacity and 1 other fieldsHigh correlation
powerHP is highly overall correlated with cubeCapacity and 3 other fieldsHigh correlation
powerKW is highly overall correlated with cubeCapacity and 3 other fieldsHigh correlation
targetPrice is highly overall correlated with powerHP and 2 other fieldsHigh correlation
type is highly overall correlated with doorNumberHigh correlation
yearIntroduced is highly overall correlated with targetPriceHigh correlation
doorNumber is highly imbalanced (56.4%)Imbalance
transmission is highly imbalanced (50.0%)Imbalance
vehicleID has unique valuesUnique

Reproduction

Analysis started2025-10-13 19:34:05.465613
Analysis finished2025-10-13 19:34:10.666419
Duration5.2 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

vehicleID
Text

Unique 

Distinct18575
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2025-10-13T14:34:10.840127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.4023149
Min length6

Characters and Unicode

Total characters174648
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18575 ?
Unique (%)100.0%

Sample

1st rowV_1232
2nd rowV_1233
3rd rowV_1234
4th rowV_1235
5th rowV_1236
ValueCountFrequency (%)
v_123171
 
< 0.1%
v_123185761
 
< 0.1%
v_12321
 
< 0.1%
v_12331
 
< 0.1%
v_12341
 
< 0.1%
v_12351
 
< 0.1%
v_12361
 
< 0.1%
v_12371
 
< 0.1%
v_12381
 
< 0.1%
v_12391
 
< 0.1%
Other values (18565)18565
99.9%
2025-10-13T14:34:11.115566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134769
19.9%
226193
15.0%
326193
15.0%
_18575
10.6%
V18575
10.6%
47618
 
4.4%
57595
 
4.3%
67518
 
4.3%
77514
 
4.3%
87084
 
4.1%
Other values (2)13014
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)174648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
134769
19.9%
226193
15.0%
326193
15.0%
_18575
10.6%
V18575
10.6%
47618
 
4.4%
57595
 
4.3%
67518
 
4.3%
77514
 
4.3%
87084
 
4.1%
Other values (2)13014
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)174648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
134769
19.9%
226193
15.0%
326193
15.0%
_18575
10.6%
V18575
10.6%
47618
 
4.4%
57595
 
4.3%
67518
 
4.3%
77514
 
4.3%
87084
 
4.1%
Other values (2)13014
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)174648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
134769
19.9%
226193
15.0%
326193
15.0%
_18575
10.6%
V18575
10.6%
47618
 
4.4%
57595
 
4.3%
67518
 
4.3%
77514
 
4.3%
87084
 
4.1%
Other values (2)13014
 
7.5%
Distinct4859
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size145.2 KiB
Minimum1990-06-18 00:00:00
Maximum2024-12-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-13T14:34:11.193329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:11.291094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

kilometers
Real number (ℝ)

Distinct16693
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177312.84
Minimum160
Maximum2795490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:11.373939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile45698.7
Q1117883.5
median169721
Q3226375.5
95-th percentile329084.1
Maximum2795490
Range2795330
Interquartile range (IQR)108492

Descriptive statistics

Standard deviation94019.896
Coefficient of variation (CV)0.53024866
Kurtosis87.895031
Mean177312.84
Median Absolute Deviation (MAD)54255
Skewness4.0894384
Sum3.293586 × 109
Variance8.8397409 × 109
MonotonicityNot monotonic
2025-10-13T14:34:11.449914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16028
 
0.2%
1987228
 
< 0.1%
1216048
 
< 0.1%
3084058
 
< 0.1%
469577
 
< 0.1%
2456107
 
< 0.1%
2143496
 
< 0.1%
1593566
 
< 0.1%
1048136
 
< 0.1%
1616
 
< 0.1%
Other values (16683)18485
99.5%
ValueCountFrequency (%)
16028
0.2%
1616
 
< 0.1%
2161
 
< 0.1%
2341
 
< 0.1%
6551
 
< 0.1%
9981
 
< 0.1%
13621
 
< 0.1%
17801
 
< 0.1%
18501
 
< 0.1%
20701
 
< 0.1%
ValueCountFrequency (%)
27954901
< 0.1%
26674311
< 0.1%
23403111
< 0.1%
22491301
< 0.1%
9829681
< 0.1%
9380481
< 0.1%
8430171
< 0.1%
8086132
< 0.1%
8060291
< 0.1%
7811951
< 0.1%

colour
Categorical

Distinct15
Distinct (%)0.1%
Missing19
Missing (%)0.1%
Memory size968.9 KiB
Grey
8733 
Black
5626 
Blue
1655 
White
1571 
Red
 
372
Other values (10)
 
599

Length

Max length8
Median length4
Mean length4.4042358
Min length1

Characters and Unicode

Total characters81725
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowRed
2nd rowRed
3rd rowRed
4th rowRed
5th rowBlack

Common Values

ValueCountFrequency (%)
Grey8733
47.0%
Black5626
30.3%
Blue1655
 
8.9%
White1571
 
8.5%
Red372
 
2.0%
Green246
 
1.3%
Brown237
 
1.3%
Beige40
 
0.2%
Yellow32
 
0.2%
Orange21
 
0.1%
Other values (5)23
 
0.1%
(Missing)19
 
0.1%

Length

2025-10-13T14:34:11.527646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grey8733
47.1%
black5626
30.3%
blue1655
 
8.9%
white1571
 
8.5%
red372
 
2.0%
green246
 
1.3%
brown237
 
1.3%
beige40
 
0.2%
yellow32
 
0.2%
orange21
 
0.1%
Other values (5)23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e12977
15.9%
r9247
11.3%
G8980
11.0%
y8733
10.7%
B7562
9.3%
l7363
9.0%
a5651
6.9%
c5626
6.9%
k5626
6.9%
u1659
 
2.0%
Other values (16)8301
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)81725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e12977
15.9%
r9247
11.3%
G8980
11.0%
y8733
10.7%
B7562
9.3%
l7363
9.0%
a5651
6.9%
c5626
6.9%
k5626
6.9%
u1659
 
2.0%
Other values (16)8301
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)81725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e12977
15.9%
r9247
11.3%
G8980
11.0%
y8733
10.7%
B7562
9.3%
l7363
9.0%
a5651
6.9%
c5626
6.9%
k5626
6.9%
u1659
 
2.0%
Other values (16)8301
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)81725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e12977
15.9%
r9247
11.3%
G8980
11.0%
y8733
10.7%
B7562
9.3%
l7363
9.0%
a5651
6.9%
c5626
6.9%
k5626
6.9%
u1659
 
2.0%
Other values (16)8301
10.2%

aestheticGrade
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size990.0 KiB
Bad
6575 
Very Bad
6203 
Medium
3623 
Good
1435 
Very Good
739 

Length

Max length9
Median length8
Mean length5.570821
Min length3

Characters and Unicode

Total characters103478
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Good
2nd rowBad
3rd rowBad
4th rowBad
5th rowBad

Common Values

ValueCountFrequency (%)
Bad6575
35.4%
Very Bad6203
33.4%
Medium3623
19.5%
Good1435
 
7.7%
Very Good739
 
4.0%

Length

2025-10-13T14:34:11.593069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T14:34:11.646035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bad12778
50.1%
very6942
27.2%
medium3623
 
14.2%
good2174
 
8.5%

Most occurring characters

ValueCountFrequency (%)
d18575
18.0%
B12778
12.3%
a12778
12.3%
e10565
10.2%
V6942
 
6.7%
r6942
 
6.7%
y6942
 
6.7%
6942
 
6.7%
o4348
 
4.2%
M3623
 
3.5%
Other values (4)13043
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)103478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d18575
18.0%
B12778
12.3%
a12778
12.3%
e10565
10.2%
V6942
 
6.7%
r6942
 
6.7%
y6942
 
6.7%
6942
 
6.7%
o4348
 
4.2%
M3623
 
3.5%
Other values (4)13043
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)103478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d18575
18.0%
B12778
12.3%
a12778
12.3%
e10565
10.2%
V6942
 
6.7%
r6942
 
6.7%
y6942
 
6.7%
6942
 
6.7%
o4348
 
4.2%
M3623
 
3.5%
Other values (4)13043
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)103478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d18575
18.0%
B12778
12.3%
a12778
12.3%
e10565
10.2%
V6942
 
6.7%
r6942
 
6.7%
y6942
 
6.7%
6942
 
6.7%
o4348
 
4.2%
M3623
 
3.5%
Other values (4)13043
12.6%

mechanicalGrade
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size978.3 KiB
Bad
7267 
Medium
4203 
Good
3574 
Very Good
1903 
Very Bad
1628 

Length

Max length9
Median length8
Mean length4.9241454
Min length3

Characters and Unicode

Total characters91466
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Good
2nd rowGood
3rd rowGood
4th rowGood
5th rowVery Good

Common Values

ValueCountFrequency (%)
Bad7267
39.1%
Medium4203
22.6%
Good3574
19.2%
Very Good1903
 
10.2%
Very Bad1628
 
8.8%

Length

2025-10-13T14:34:11.715936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T14:34:11.775618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bad8895
40.2%
good5477
24.8%
medium4203
19.0%
very3531
 
16.0%

Most occurring characters

ValueCountFrequency (%)
d18575
20.3%
o10954
12.0%
a8895
9.7%
B8895
9.7%
e7734
8.5%
G5477
 
6.0%
i4203
 
4.6%
M4203
 
4.6%
m4203
 
4.6%
u4203
 
4.6%
Other values (4)14124
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)91466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d18575
20.3%
o10954
12.0%
a8895
9.7%
B8895
9.7%
e7734
8.5%
G5477
 
6.0%
i4203
 
4.6%
M4203
 
4.6%
m4203
 
4.6%
u4203
 
4.6%
Other values (4)14124
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d18575
20.3%
o10954
12.0%
a8895
9.7%
B8895
9.7%
e7734
8.5%
G5477
 
6.0%
i4203
 
4.6%
M4203
 
4.6%
m4203
 
4.6%
u4203
 
4.6%
Other values (4)14124
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d18575
20.3%
o10954
12.0%
a8895
9.7%
B8895
9.7%
e7734
8.5%
G5477
 
6.0%
i4203
 
4.6%
M4203
 
4.6%
m4203
 
4.6%
u4203
 
4.6%
Other values (4)14124
15.4%
Distinct521
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size145.2 KiB
Minimum1932-09-02 00:00:00
Maximum2031-11-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-13T14:34:11.855828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:11.956459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

make
Categorical

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1002.2 KiB
RENAULT
2255 
VOLKSWAGEN
1804 
PEUGEOT
1740 
BMW
1493 
OPEL
1462 
Other values (35)
9821 

Length

Max length13
Median length10
Mean length6.2418304
Min length2

Characters and Unicode

Total characters115942
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHYUNDAI
2nd rowNISSAN
3rd rowNISSAN
4th rowNISSAN
5th rowVOLKSWAGEN

Common Values

ValueCountFrequency (%)
RENAULT2255
12.1%
VOLKSWAGEN1804
 
9.7%
PEUGEOT1740
 
9.4%
BMW1493
 
8.0%
OPEL1462
 
7.9%
MERCEDES-BENZ1020
 
5.5%
CITROEN993
 
5.3%
AUDI800
 
4.3%
FORD796
 
4.3%
FIAT785
 
4.2%
Other values (30)5427
29.2%

Length

2025-10-13T14:34:12.043074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
renault2255
12.0%
volkswagen1804
 
9.6%
peugeot1740
 
9.3%
bmw1493
 
8.0%
opel1462
 
7.8%
mercedes-benz1020
 
5.4%
citroen993
 
5.3%
audi800
 
4.3%
ford796
 
4.2%
fiat785
 
4.2%
Other values (31)5605
29.9%

Most occurring characters

ValueCountFrequency (%)
E15300
13.2%
O10821
 
9.3%
A10241
 
8.8%
T8384
 
7.2%
N8347
 
7.2%
L6657
 
5.7%
S5927
 
5.1%
R5772
 
5.0%
U5337
 
4.6%
I4817
 
4.2%
Other values (17)34339
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)115942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E15300
13.2%
O10821
 
9.3%
A10241
 
8.8%
T8384
 
7.2%
N8347
 
7.2%
L6657
 
5.7%
S5927
 
5.1%
R5772
 
5.0%
U5337
 
4.6%
I4817
 
4.2%
Other values (17)34339
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)115942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E15300
13.2%
O10821
 
9.3%
A10241
 
8.8%
T8384
 
7.2%
N8347
 
7.2%
L6657
 
5.7%
S5927
 
5.1%
R5772
 
5.0%
U5337
 
4.6%
I4817
 
4.2%
Other values (17)34339
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)115942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E15300
13.2%
O10821
 
9.3%
A10241
 
8.8%
T8384
 
7.2%
N8347
 
7.2%
L6657
 
5.7%
S5927
 
5.1%
R5772
 
5.0%
U5337
 
4.6%
I4817
 
4.2%
Other values (17)34339
29.6%

model
Text

Distinct283
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1006.1 KiB
2025-10-13T14:34:12.221878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length11
Mean length4.9831494
Min length1

Characters and Unicode

Total characters92562
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.2%

Sample

1st rowKauai
2nd rowJuke
3rd rowJuke
4th rowJuke
5th rowGolf
ValueCountFrequency (%)
mégan1148
 
5.4%
classe1016
 
4.8%
clio787
 
3.7%
golf696
 
3.3%
astra695
 
3.3%
c658
 
3.1%
serie-3607
 
2.9%
polo602
 
2.8%
corsa570
 
2.7%
ibiza443
 
2.1%
Other values (274)14044
66.0%
2025-10-13T14:34:12.480729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a8551
 
9.2%
o6201
 
6.7%
i6086
 
6.6%
s5937
 
6.4%
e5897
 
6.4%
C4830
 
5.2%
r4503
 
4.9%
l3773
 
4.1%
n3146
 
3.4%
03032
 
3.3%
Other values (56)40606
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)92562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a8551
 
9.2%
o6201
 
6.7%
i6086
 
6.6%
s5937
 
6.4%
e5897
 
6.4%
C4830
 
5.2%
r4503
 
4.9%
l3773
 
4.1%
n3146
 
3.4%
03032
 
3.3%
Other values (56)40606
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)92562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a8551
 
9.2%
o6201
 
6.7%
i6086
 
6.6%
s5937
 
6.4%
e5897
 
6.4%
C4830
 
5.2%
r4503
 
4.9%
l3773
 
4.1%
n3146
 
3.4%
03032
 
3.3%
Other values (56)40606
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)92562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a8551
 
9.2%
o6201
 
6.7%
i6086
 
6.6%
s5937
 
6.4%
e5897
 
6.4%
C4830
 
5.2%
r4503
 
4.9%
l3773
 
4.1%
n3146
 
3.4%
03032
 
3.3%
Other values (56)40606
43.9%

doorNumber
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size907.1 KiB
5
14601 
3
1922 
4
1882 
2
 
155
6
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18575
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
514601
78.6%
31922
 
10.3%
41882
 
10.1%
2155
 
0.8%
615
 
0.1%

Length

2025-10-13T14:34:12.550311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T14:34:12.598534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
514601
78.6%
31922
 
10.3%
41882
 
10.1%
2155
 
0.8%
615
 
0.1%

Most occurring characters

ValueCountFrequency (%)
514601
78.6%
31922
 
10.3%
41882
 
10.1%
2155
 
0.8%
615
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)18575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
514601
78.6%
31922
 
10.3%
41882
 
10.1%
2155
 
0.8%
615
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
514601
78.6%
31922
 
10.3%
41882
 
10.1%
2155
 
0.8%
615
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
514601
78.6%
31922
 
10.3%
41882
 
10.1%
2155
 
0.8%
615
 
0.1%

type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Hatchback
10189 
Estate
5970 
Sedan
1834 
Coupe
 
582

Length

Max length9
Median length9
Mean length7.5155316
Min length5

Characters and Unicode

Total characters139601
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHatchback
2nd rowHatchback
3rd rowHatchback
4th rowHatchback
5th rowEstate

Common Values

ValueCountFrequency (%)
Hatchback10189
54.9%
Estate5970
32.1%
Sedan1834
 
9.9%
Coupe582
 
3.1%

Length

2025-10-13T14:34:12.669221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T14:34:12.724686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hatchback10189
54.9%
estate5970
32.1%
sedan1834
 
9.9%
coupe582
 
3.1%

Most occurring characters

ValueCountFrequency (%)
a28182
20.2%
t22129
15.9%
c20378
14.6%
H10189
 
7.3%
h10189
 
7.3%
b10189
 
7.3%
k10189
 
7.3%
e8386
 
6.0%
E5970
 
4.3%
s5970
 
4.3%
Other values (7)7830
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)139601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a28182
20.2%
t22129
15.9%
c20378
14.6%
H10189
 
7.3%
h10189
 
7.3%
b10189
 
7.3%
k10189
 
7.3%
e8386
 
6.0%
E5970
 
4.3%
s5970
 
4.3%
Other values (7)7830
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a28182
20.2%
t22129
15.9%
c20378
14.6%
H10189
 
7.3%
h10189
 
7.3%
b10189
 
7.3%
k10189
 
7.3%
e8386
 
6.0%
E5970
 
4.3%
s5970
 
4.3%
Other values (7)7830
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a28182
20.2%
t22129
15.9%
c20378
14.6%
H10189
 
7.3%
h10189
 
7.3%
b10189
 
7.3%
k10189
 
7.3%
e8386
 
6.0%
E5970
 
4.3%
s5970
 
4.3%
Other values (7)7830
 
5.6%

fuel
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size998.0 KiB
Diesel
12466 
Petrol
6016 
Electric
 
93

Length

Max length8
Median length6
Mean length6.0100135
Min length6

Characters and Unicode

Total characters111636
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPetrol
2nd rowDiesel
3rd rowDiesel
4th rowDiesel
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel12466
67.1%
Petrol6016
32.4%
Electric93
 
0.5%

Length

2025-10-13T14:34:12.800514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T14:34:13.136775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel12466
67.1%
petrol6016
32.4%
electric93
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e31041
27.8%
l18575
16.6%
i12559
11.2%
D12466
11.2%
s12466
11.2%
t6109
 
5.5%
r6109
 
5.5%
P6016
 
5.4%
o6016
 
5.4%
c186
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)111636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e31041
27.8%
l18575
16.6%
i12559
11.2%
D12466
11.2%
s12466
11.2%
t6109
 
5.5%
r6109
 
5.5%
P6016
 
5.4%
o6016
 
5.4%
c186
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)111636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e31041
27.8%
l18575
16.6%
i12559
11.2%
D12466
11.2%
s12466
11.2%
t6109
 
5.5%
r6109
 
5.5%
P6016
 
5.4%
o6016
 
5.4%
c186
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)111636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e31041
27.8%
l18575
16.6%
i12559
11.2%
D12466
11.2%
s12466
11.2%
t6109
 
5.5%
r6109
 
5.5%
P6016
 
5.4%
o6016
 
5.4%
c186
 
0.2%

transmission
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1003.8 KiB
Manual
16532 
Automatic
2043 

Length

Max length9
Median length6
Mean length6.3299596
Min length6

Characters and Unicode

Total characters117579
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Manual16532
89.0%
Automatic2043
 
11.0%

Length

2025-10-13T14:34:13.201824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T14:34:13.252424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual16532
89.0%
automatic2043
 
11.0%

Most occurring characters

ValueCountFrequency (%)
a35107
29.9%
u18575
15.8%
M16532
14.1%
n16532
14.1%
l16532
14.1%
t4086
 
3.5%
A2043
 
1.7%
o2043
 
1.7%
m2043
 
1.7%
i2043
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)117579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a35107
29.9%
u18575
15.8%
M16532
14.1%
n16532
14.1%
l16532
14.1%
t4086
 
3.5%
A2043
 
1.7%
o2043
 
1.7%
m2043
 
1.7%
i2043
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a35107
29.9%
u18575
15.8%
M16532
14.1%
n16532
14.1%
l16532
14.1%
t4086
 
3.5%
A2043
 
1.7%
o2043
 
1.7%
m2043
 
1.7%
i2043
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a35107
29.9%
u18575
15.8%
M16532
14.1%
n16532
14.1%
l16532
14.1%
t4086
 
3.5%
A2043
 
1.7%
o2043
 
1.7%
m2043
 
1.7%
i2043
 
1.7%

yearIntroduced
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.9496
Minimum1985
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:13.313809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1998
Q12003
median2007
Q32011
95-th percentile2016
Maximum2020
Range35
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.192612
Coefficient of variation (CV)0.0025873156
Kurtosis-0.34724081
Mean2006.9496
Median Absolute Deviation (MAD)4
Skewness-0.11338301
Sum37279089
Variance26.963219
MonotonicityNot monotonic
2025-10-13T14:34:13.396284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20081614
 
8.7%
20051516
 
8.2%
20091484
 
8.0%
20071399
 
7.5%
20041215
 
6.5%
20121074
 
5.8%
20061046
 
5.6%
20031006
 
5.4%
2001904
 
4.9%
2013898
 
4.8%
Other values (24)6419
34.6%
ValueCountFrequency (%)
19855
 
< 0.1%
19871
 
< 0.1%
19892
 
< 0.1%
19901
 
< 0.1%
199114
 
0.1%
199228
 
0.2%
199357
 
0.3%
199440
 
0.2%
199549
 
0.3%
1996143
0.8%
ValueCountFrequency (%)
20202
 
< 0.1%
201925
 
0.1%
2018147
 
0.8%
2017237
 
1.3%
2016563
3.0%
2015544
2.9%
2014645
3.5%
2013898
4.8%
20121074
5.8%
2011547
2.9%

cylinder
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.460511
Minimum0
Maximum50
Zeros93
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:13.471324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q112
median15
Q319
95-th percentile21
Maximum50
Range50
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0429799
Coefficient of variation (CV)0.26150363
Kurtosis4.7118978
Mean15.460511
Median Absolute Deviation (MAD)3
Skewness0.86946656
Sum287179
Variance16.345687
MonotonicityNot monotonic
2025-10-13T14:34:13.549950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
163295
17.7%
122889
15.6%
202746
14.8%
152429
13.1%
142300
12.4%
11952
 
5.1%
19903
 
4.9%
10864
 
4.7%
21439
 
2.4%
17316
 
1.7%
Other values (29)1442
7.8%
ValueCountFrequency (%)
093
 
0.5%
610
 
0.1%
731
 
0.2%
8127
 
0.7%
9151
 
0.8%
10864
 
4.7%
11952
 
5.1%
122889
15.6%
13250
 
1.3%
142300
12.4%
ValueCountFrequency (%)
503
< 0.1%
481
 
< 0.1%
471
 
< 0.1%
447
< 0.1%
431
 
< 0.1%
423
< 0.1%
411
 
< 0.1%
403
< 0.1%
391
 
< 0.1%
361
 
< 0.1%

cubeCapacity
Real number (ℝ)

High correlation 

Distinct212
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1540.2247
Minimum0
Maximum4966
Zeros93
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:13.627979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q11248
median1461
Q31870
95-th percentile2143
Maximum4966
Range4966
Interquartile range (IQR)622

Descriptive statistics

Standard deviation397.66174
Coefficient of variation (CV)0.25818424
Kurtosis5.128262
Mean1540.2247
Median Absolute Deviation (MAD)215
Skewness0.95676335
Sum28609673
Variance158134.86
MonotonicityNot monotonic
2025-10-13T14:34:13.718691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14612049
 
11.0%
15601565
 
8.4%
15981392
 
7.5%
19951117
 
6.0%
1248954
 
5.1%
1896630
 
3.4%
1968623
 
3.4%
1198583
 
3.1%
1398582
 
3.1%
1242491
 
2.6%
Other values (202)8589
46.2%
ValueCountFrequency (%)
093
0.5%
59910
 
0.1%
69831
 
0.2%
79624
 
0.1%
799103
0.6%
87513
 
0.1%
898132
0.7%
8996
 
< 0.1%
95412
 
0.1%
9736
 
< 0.1%
ValueCountFrequency (%)
49663
< 0.1%
48061
 
< 0.1%
46631
 
< 0.1%
43983
< 0.1%
43954
< 0.1%
42931
 
< 0.1%
41962
< 0.1%
41721
 
< 0.1%
41341
 
< 0.1%
39972
< 0.1%

powerKW
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.892544
Minimum29
Maximum412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:13.801752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile45
Q155
median74
Q388
95-th percentile132
Maximum412
Range383
Interquartile range (IQR)33

Descriptive statistics

Standard deviation28.744424
Coefficient of variation (CV)0.36902665
Kurtosis8.4773965
Mean77.892544
Median Absolute Deviation (MAD)15
Skewness1.9533692
Sum1446854
Variance826.24193
MonotonicityNot monotonic
2025-10-13T14:34:13.890244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
661905
 
10.3%
551521
 
8.2%
811381
 
7.4%
77860
 
4.6%
51847
 
4.6%
50698
 
3.8%
80663
 
3.6%
70647
 
3.5%
85601
 
3.2%
110595
 
3.2%
Other values (127)8857
47.7%
ValueCountFrequency (%)
296
 
< 0.1%
3045
 
0.2%
3329
 
0.2%
37127
0.7%
3824
 
0.1%
3940
 
0.2%
4072
0.4%
415
 
< 0.1%
424
 
< 0.1%
4387
0.5%
ValueCountFrequency (%)
4123
< 0.1%
3201
 
< 0.1%
3171
 
< 0.1%
3152
< 0.1%
3001
 
< 0.1%
2941
 
< 0.1%
2804
< 0.1%
2721
 
< 0.1%
2703
< 0.1%
2581
 
< 0.1%

powerHP
Real number (ℝ)

High correlation 

Distinct160
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.99736
Minimum39
Maximum560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:13.981601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile61
Q175
median100
Q3120
95-th percentile180
Maximum560
Range521
Interquartile range (IQR)45

Descriptive statistics

Standard deviation39.065234
Coefficient of variation (CV)0.36854912
Kurtosis8.4892257
Mean105.99736
Median Absolute Deviation (MAD)20
Skewness1.9561873
Sum1968901
Variance1526.0925
MonotonicityNot monotonic
2025-10-13T14:34:14.072255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901882
 
10.1%
751534
 
8.3%
1101409
 
7.6%
1051049
 
5.6%
95648
 
3.5%
109638
 
3.4%
68620
 
3.3%
150587
 
3.2%
70582
 
3.1%
115554
 
3.0%
Other values (150)9072
48.8%
ValueCountFrequency (%)
395
 
< 0.1%
401
 
< 0.1%
4145
 
0.2%
4529
 
0.2%
50127
0.7%
5123
 
0.1%
521
 
< 0.1%
531
 
< 0.1%
5480
0.4%
5536
 
0.2%
ValueCountFrequency (%)
5603
< 0.1%
4351
 
< 0.1%
4311
 
< 0.1%
4282
< 0.1%
4071
 
< 0.1%
4001
 
< 0.1%
3814
< 0.1%
3701
 
< 0.1%
3673
< 0.1%
3501
 
< 0.1%

targetPrice
Real number (ℝ)

High correlation 

Distinct407
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5960.217
Minimum400
Maximum60200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.2 KiB
2025-10-13T14:34:14.153807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile850
Q12000
median4100
Q37700
95-th percentile17000
Maximum60200
Range59800
Interquartile range (IQR)5700

Descriptive statistics

Standard deviation6021.3456
Coefficient of variation (CV)1.0102561
Kurtosis10.856872
Mean5960.217
Median Absolute Deviation (MAD)2400
Skewness2.7156971
Sum1.1071103 × 108
Variance36256603
MonotonicityNot monotonic
2025-10-13T14:34:14.235651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800402
 
2.2%
1300322
 
1.7%
1700301
 
1.6%
1200295
 
1.6%
1600294
 
1.6%
1100277
 
1.5%
2300272
 
1.5%
1000270
 
1.5%
1900268
 
1.4%
2000267
 
1.4%
Other values (397)15607
84.0%
ValueCountFrequency (%)
40030
 
0.2%
45044
 
0.2%
500115
0.6%
5301
 
< 0.1%
55033
 
0.2%
600136
0.7%
65039
 
0.2%
700202
1.1%
75046
 
0.2%
800244
1.3%
ValueCountFrequency (%)
602001
< 0.1%
578002
< 0.1%
576001
< 0.1%
574001
< 0.1%
573001
< 0.1%
569001
< 0.1%
566001
< 0.1%
526001
< 0.1%
523002
< 0.1%
500001
< 0.1%

Interactions

2025-10-13T14:34:09.879522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:06.741761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.203169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.715845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.171302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.661413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.407963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.948585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:06.803597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.281288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.777629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.236847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.733037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.473679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:10.027045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:06.870315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.354534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.854488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.311190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.057686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.547942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:10.098666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:06.933578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.420618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.913330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.386255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.130456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.612364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:10.181847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:06.995480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.486686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.977820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.456522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.199536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.676839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:10.253108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.066221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.567969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.042708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.525363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.271099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.749318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:10.322323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.129630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:07.637825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.107731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:08.591196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.337045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T14:34:09.808980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-13T14:34:14.304829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
aestheticGradecolourcubeCapacitycylinderdoorNumberfuelkilometersmakemechanicalGradepowerHPpowerKWtargetPricetransmissiontypeyearIntroduced
aestheticGrade1.0000.0800.0790.0730.0170.1010.0560.1370.2100.1060.1060.2280.1770.0380.215
colour0.0801.0000.0660.0710.0910.1240.0510.1010.0790.0540.0540.0640.0580.1130.116
cubeCapacity0.0790.0661.0000.9930.2180.7930.3190.3830.0840.8460.8440.3580.4640.3530.085
cylinder0.0730.0710.9931.0000.2160.8060.3220.3560.0890.8470.8450.3520.4390.3380.078
doorNumber0.0170.0910.2180.2161.0000.1320.0690.3090.0440.1900.1910.0840.1310.6840.103
fuel0.1010.1240.7930.8060.1321.0000.1270.3070.0870.3390.3370.2010.2380.2720.239
kilometers0.0560.0510.3190.3220.0690.1271.0000.0780.1460.1510.151-0.4420.0000.106-0.459
make0.1370.1010.3830.3560.3090.3070.0781.0000.1560.3310.3290.2890.4660.4510.162
mechanicalGrade0.2100.0790.0840.0890.0440.0870.1460.1561.0000.1000.1000.2950.2350.0590.323
powerHP0.1060.0540.8460.8470.1900.3390.1510.3310.1001.0000.9990.5310.4960.3110.287
powerKW0.1060.0540.8440.8450.1910.3370.1510.3290.1000.9991.0000.5300.4960.3130.285
targetPrice0.2280.0640.3580.3520.0840.201-0.4420.2890.2950.5310.5301.0000.4690.1030.837
transmission0.1770.0580.4640.4390.1310.2380.0000.4660.2350.4960.4960.4691.0000.2790.253
type0.0380.1130.3530.3380.6840.2720.1060.4510.0590.3110.3130.1030.2791.0000.113
yearIntroduced0.2150.1160.0850.0780.1030.239-0.4590.1620.3230.2870.2850.8370.2530.1131.000

Missing values

2025-10-13T14:34:10.441644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-13T14:34:10.574160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

vehicleIDregistrationDatekilometerscolouraestheticGrademechanicalGradesaleDatemakemodeldoorNumbertypefueltransmissionyearIntroducedcylindercubeCapacitypowerKWpowerHPtargetPrice
0V_123223/01/20228984RedVery GoodVery Good07/11/2022HYUNDAIKauai5HatchbackPetrolManual2017109988812017300
1V_123323/01/2017127566RedBadGood20/08/2022NISSANJuke5HatchbackDieselManual2010151461811108800
2V_123423/01/2017127566RedBadGood13/09/2022NISSANJuke5HatchbackDieselManual2010151461811109600
3V_123523/01/2017127566RedBadGood16/08/2022NISSANJuke5HatchbackDieselManual2010151461811108500
4V_123623/01/2016108759BlackBadVery Good09/05/2022VOLKSWAGENGolf5EstateDieselAutomatic20131615987710511300
5V_123723/01/2016185798BlackBadMedium01/06/2022NISSANQashqai5EstateDieselManual20091615989613012200
6V_123823/01/2016185798BlackBadMedium08/06/2022NISSANQashqai5EstateDieselManual20091615989613011700
7V_123923/01/2013179959BlackBadMedium18/05/2022RENAULTClio5EstateDieselManual200915146166903900
8V_1231023/01/2013195703GreyMediumBad16/11/2022FORDFocus5EstateDieselManual2008161560801092700
9V_1231123/01/2013197582BlackVery BadBad28/08/2022ALFA ROMEOGiulietta5HatchbackDieselManual2010161598771054900
vehicleIDregistrationDatekilometerscolouraestheticGrademechanicalGradesaleDatemakemodeldoorNumbertypefueltransmissionyearIntroducedcylindercubeCapacitypowerKWpowerHPtargetPrice
18565V_1231856714/09/201671420GreyBadMedium11/10/2023TOYOTAAuris5EstateDieselManual2013141364669010500
18566V_1231856822/06/2008253884BlackVery BadVery Bad18/10/2023MERCEDES-BENZClasse C5EstateDieselManual20012121481051433500
18567V_1231856910/09/2005135036GreyVery BadGood16/10/2023AUDIA45EstateDieselAutomatic2001191896961305300
18568V_1231857019/08/2014197823BlackBadBad11/10/2023NISSANQashqai5EstateDieselManual2009151461811098700
18569V_1231857118/12/2010190766GreyBadBad11/10/2023AUDIA35EstateDieselManual20082019681031406700
18570V_1231857222/08/2005190913BlackBadBad14/10/2023AUDIA45EstatePetrolManual2001161595751022500
18571V_1231857325/08/2020278484GreyMediumMedium11/10/2023RENAULTMégan5EstateDieselManual2016151461811108800
18572V_1231857423/07/2017157217GreyVery BadGood11/10/2023SEATLeon5EstateDieselManual2013161598811109700
18573V_1231857521/06/2017156823GreyBadVery Good11/10/2023AUDIA65EstateDieselAutomatic201420196814019018900
18574V_1231857620/11/2013131553BlackVery BadMedium16/10/2023SEATIbiza5EstatePetrolManual201012119851704300